In this section, we provide several prior works that explored the fine motor skills and fine motor patterns of children with ASD.
4.3.1 Related Work on Investigating Fine Motor Skills of Children with ASD
Given limited ASD intervention resources in the healthcare system [24] and with the documented affinity of many children with ASD for technology [25], a number of computer-assisted systems have been
developed in recent years that provide an attractive, replicable, low-cost, quantitatively measurable, and controlled intervention environment with real-time feedback [26-29]. Fine motor tasks require a longer duration of attention and physical effort. Fine motor tasks in the form of computer games can deliver consistent and real-time rewards or consequences for responses, which can encourage engagement and retain children’s attention [30]. For example, the haptic assisted handwriting training system developed by Kim et al. [31] provided a reward and punishment system, including a score relating to the user’s performance, to encourage the user to explore increasingly challenging tasks. Haptic devices are capable of providing human-computer interactions through body contact and responding to human behaviors with mechanical force feedback. Several studies have reported that haptic devices are beneficial in increasing immersion and quality of task performance and thus enhancing training achievements [32-37]. For example, Khurshid et al. [32] examined the effects of grip-force, contact and acceleration feedback on a teleoperated pick-and-place task with a haptic device worn on the participant’s hand. They found that grip-force and high-frequency acceleration feedback had positive effects on task performance. Sutherland et al. [33]
proposed a haptic simulation system for spinal needle insertion training, and demonstrated that the system had the potential for reducing the need for the presence of live patients or cadavers or trained clinician.
Morris et al. [34] explored the use of haptic feedback for improving the learning accuracy of force recall, and demonstrated that the combined visuohaptic training was more effective than either visual or haptic training alone. Gupta et al. [35] made a haptic arm exoskeleton for providing safe and repeatable rehabilitation and training in virtual environments. Patton et al. [36] showed the potential of haptic training in helping to restore reaching ability of patients with post-stroke hemiparesis. Patomäki et al. [37] designed a series of multimodal applications with haptic interface for visually impaired children to learn and play.
Research in computer-based systems to investigate the fine motor ability of children with ASD are diverse with respect to the targeted deficits, the instruments involved, and the measures and analysis used.
A common way of assessing children’s fine motor skills is through handwriting analysis [31, 38-42]. For example, Rosenblum et al. [38] indicated the unique handwriting characteristics of children with high- functioning autism spectrum disorder (HFASD) (age: 9-12) by using a computerized instrumentation consisting of a tablet, where the user performed writing tasks. They suggested that identifying ASD-specific handwriting features could provide a more comprehensive picture of individual deficits, and may contribute to more focused and adaptive intervention. Palsbo et al. [39] provided repetitive fine motor training of hand using a Falcon haptic device for children with fine motor deficits including children with ASD (age: 5-11), aiming to improve their handwriting legibility and speed. The haptic device was programmed to generate a 3D pathway when the user typed in letters, numbers or punctuation glyphs. They reported that this kind of hand training helped improve handwriting fluidity of children with ASD. Kim et al. [31] also developed a haptic assisted training (HAT) system for transferring and improving handwriting skill. The HAT system
guided the user’s hand along a sequence of strokes and provided the training tasks in the form of 3D games.
They implemented the systems with two different haptic devices, Phantom Omni and Novint Falcon, and tested the systems with children (age: 6-11) grouped by typical and special need. The system was found to be well received by the children, who showed improvements in tracing alphabets. Johnson et al. [40]
investigated the underlying factors (specifically relating to motor control) of macrographia in children with ASD (age: 8-13) by asking them to write a series of cursive letters using a tablet and a stylus. They observed significant instability of fundamental handwriting movements and atypical biomechanical strategies that contributed to larger and less consistent handwriting in children with ASD. Palluel-Germain et al. [41]
conducted a study to indicate that the visuo-haptic device may increase the fluency of handwriting production of cursive letters in kindergarten age children (age: 5). Pernalete et al. [42] explored the possibility of improving eye-hand coordination in children with limited motor skills using a haptic interface, which could provide force feedback as well as inertia and viscosity effects. They found improved motor accuracy in participants after the haptic therapy.
While the overall hand manipulation was considered in the above-mentioned studies, few have investigated the training of grip force and grip patterns for fine motor manipulations. However, the importance of grip force in fine motor manipulation and atypical grip control in children with ASD is well- documented [6, 43-46]. Wang et al. [43] examined the grip force control of children with ASD (age: 5-15) by asking children to press on opposing load cells with their thumb and index finger. They found some distinct grip patterns between the children with ASD and the TD children, such as increased force variability among the ASD group when trying to sustain a constant force level. David et al. [44] investigated the grip and load force adjustments in children with HFASD (age: 8-19) using a precision grip task that required the children to pick up a target from a starting position, and placed it to a target position. They observed greater grip force at movement onset and a more variable performance of the ASD group as compared to the TD group. Falk et al. [45] developed a computer-based handwriting assessment tool consisting of a custom-built pen with pressure sensors, which could achieve grip force measurement during writing. They used this tool to objectively quantify handwriting proficiency and detect handwriting difficulties in children, and found that grip force describing the grip strategy and dynamics could be a useful indicator of handwriting legibility. Abu-Dahab [46] examined motor and tactile-perceptual skills in individuals with high-functioning autism (IHFA) (age: 5-21), and found impaired grip strength, motor speed and coordination in IHFA. They suggested that the assessment and intervention of motor and tactile-perceptual skills should be performed early because these skills are essential to school performance.
It can be seen from the aforementioned literature survey that a large number of children with ASD have deficits in fine motor skills, in part due to difficulties with precise hand motion control and steady grip control. However, to our knowledge, there is no other existing system that provides opportunities for both
(1) quantitatively assessing fine motor deficits, and (2) practicing such skills using systematically designed tasks that require both hand motion and grip control in a virtual environment with haptic feedback.
Considering the importance of haptic immersion as well as grip control in addressing fine motor impairment of children with ASD, we present a novel human-computer interaction system that combines a haptic device with a custom designed grip control system and integrates them with a virtual reality environment where fine motor tasks can be practiced with real-time force and audio-visual feedback. We develop this system, called Hg, in the hope of expanding accessibility of efficient motor interventions for children with ASD.
4.3.2 Related Work on Investigating Fine Motor Patterns of Children with ASD
ASD diagnosis is a difficult and complex task due to the wide range of symptoms involved. Currently, ASD diagnosis relies on the clinical evaluation of autism-specific behaviors via standardized interviews, observations and questionnaires, which is often time- and resource-consuming [47]. As a growing number of studies evidence the existence of atypical motor patterns in children with ASD, understanding and exploring the motor signatures in children with ASD provide a new methodology to facilitate ASD diagnosis [22]. Currently, computer-assisted systems have been increasingly applied in ASD intervention taking advantage of providing an engaging intervention environment as well as recording objective and quantitative performance data [27, 29, 48]. The equipment with specific sensors to detect and measure motor information is easily accessible in recent days. Instead of using paper materials for motor function assessment, such as Beery VMI [23] and Mullen Scales [49], motor tasks in the form of computer tasks/games are more likely to be accepted among children with ASD, and are able to provide computational measures enabling the exploration of motor patterns using pattern recognition approaches.
Studies have used a variety of motor tasks to explore the motor signature of children with ASD. For example, Anzulewicz et al. identified children with ASD employing machine learning analysis of movement data from a tablet gameplay, and achieved a maximum classification accuracy of 93% [50].
Wedyan et al. found good classification results between high risk infants and low risk infants for autism by using data from an upper-limb movement task [51]. Crippa et al. used the data from a reach-to-drop task to classify children with ASD aged 2-4 from TD children, and obtained a high classification accuracy up to 96.7% using Support Vector Machine (SVM) [52]. Johnson et al. examined the handwriting difficulties of children with ASD relating to motor control, and found children with ASD had larger stroke heights, instability of movements and faster movement speed as compared to TD controls [40]. Calhoun et al. [53]
identified the gait patterns using kinematic analysis that were significantly different between children with ASD and TD controls. Among the existing studies, most of them used motion tracking equipment that put several markers on the subject’s body to collect motor information, which may cause interference, obstruct natural movement and require complex operations. Only a few studies employ embedded sensors and
integrated devices to support a more comfortable and natural intervention environment [45, 50]. In addition, there is less research on investigating the subtle fine motor patterns of children with ASD. However, the increased need for early ASD diagnosis and the growing evidence that limited fine motor skills in infants with ASD [7, 54] indicate the importance and significance in exploring the fine motor predictor of ASD.
For example, the work of LeBarton et al. suggested that fine motor skills could predict expressive language skills in infant siblings of children with ASD [55]. Gernsbacher et al. demonstrated the associations among early oral- and manual-motor skills and later speech fluency [56].